Performance improvements for evolutionary strategy-based one-class constraint synthesis
Tomasz P. Pawlak · Proceedings of the Genetic and Evolutionary Computation Conference · 2018
Mathematical Programming (MP) models are common in optimization of systems. Designing those models, however, is challenging for human experts facing deficiencies in domain knowledge, rigorous technical requirements for the model (e.g., linearity) or lack of experience. Evolutionary Strategy-based One-Class Constraint Synthesis (ESOCCS) is a recently proposed method for computer-aided modeling, aimed at reduction of the burden on the expert by acquiring the MP constraints from historical data and letting the expert to freely modify them, supplement with an objective function and optimize using an off-the-shelf solver. In this study, we extend ESOCCS with five improvements aimed at increasing its performance in typical problems. Three of them turn out beneficial in a rigorous experimental evaluation and one prevents ESOCCS from producing degenerate models.